ABSTRACT Adverse events (AEs) ? harm to patients that results from medical care ? affect as many as 13.5% of hospitalized patients; half of these AEs are preventable and AEs particularly affect the elderly. AEs are notoriously difficult to measure accurately. A variety of paper and electronic trigger tools have been developed to identify AEs; however, their positive predictive value (PPV) is low, requiting subsequent, time-intensive manual chart review to accurately measure AEs. In the proposed project, we will use innovative, state-of-the-art machine interactive learning (IML) techniques to refine existing AE triggers, improving their accuracy substantially. We will also develop a novel AE Explorer to speed review of possible AEs, as well as an innovative package of predictive analytics tools and methods to measure and detect them. Our approach combines and compares expert-driven improvement with the most recent IML techniques to make triggers more accurate, with the ultimate goal of creating triggers that are accurate enough to stand in as proxies for actual measurement of harm. We call our approach Safety Promotion through Early Event Detection in the Elderly, or SPEEDe. Our team of accomplished machine learning, patient safety, risk management, AE detection, geriatric medicine and trigger tool experts will work together to carry out the specific aims of this project: (1) prototype and rapidly iterate a trigger review dashboard (the Adverse Event Explorer) using a user-centered design process, (2) develop and evaluate novel Interactive Machine Learning approaches for more efficient and accurate adverse event chart review and trigger refinement, and (3) Integrate Interactive Machine Learning into the Adverse Event Explorer and evaluate it prospectively in a clinical setting.